An Intrusion Detection Method Based on Multi-level Feature Selection for Industrial Internet
College of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
| Abstract: | To address the limited detection accuracy of industrial Internet intrusion detection systems caused by feature redundancy and inadequate feature representation, this study proposes a multi-stage feature selection-based intrusion detection method for the industrial Internet. First, the Pearson correlation coefficient is employed to quantify the linear correlations among features, and highly correlated redundant features are eliminated according to a predefined correlation threshold, thereby achieving preliminary dimensionality reduction. Subsequently, an adaptive elite genetic algorithm, incorporating adaptive crossover and mutation probabilities together with an elite preservation strategy, is introduced to further optimize the feature subset. A fitness function that jointly considers classification performance and the number of selected features is designed to identify the optimal feature subset. Experiments are conducted on the publicly available TON-IoT network dataset. The proposed two-stage feature selection strategy reduces the original 38 features to 26, achieving a feature reduction rate of 31.58% while effectively alleviating feature redundancy. Under identical classification settings, the proposed method demonstrates superior detection performance with both Random Forest and K-Nearest Neighbors classifiers compared with mutual information- and particle swarm optimization-based feature selection methods. Furthermore, to exploit the discriminative information contained in the selected features and further improve detection accuracy, a CNN-GRU hybrid model is developed based on the optimized feature subset. The proposed CNN-GRU model achieves a detection accuracy of 95.61%, outperforming the standalone CNN and GRU models by 2.19% and 0.47%, respectively. These results demonstrate the effectiveness of the proposed framework in reducing feature redundancy and improving intrusion detection performance, while reducing the dimensionality of the model input and associated computational overhead. The proposed approach provides a potential solution for efficient and accurate intrusion detection in resource-constrained industrial environments. |
| Keywords: | Intrusion Detection; Multi-Level Feature Selection; Genetic Algorithm; Deep Learning |
| DOI: | 10.57237/j.cst.2026.04.001 |
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Received: 26 July 2026
Accepted: 17 August 2026
Published Online: 2 November 2026
References
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